Statistics - Distribution Function Technique

In summary, the p.d.f. of Y is exponential with \theta = 2 and the answer given in the back of the book provides the MGF (moment-generating function) of Y. The MGF is given as M(t) = (1-2t)^{-1} for t<1/2, which is a familiar and recognizable form. However, there are other ways to solve this problem.
  • #1
Addem
20
0

Homework Statement



(From Probability and Statistical Inference, Hogg and Tanis, Eighth Edition, 5.1-5)

The p.d.f. of X is [itex]f(x) = \theta x^{\theta - 1}[/itex] for [itex]0<x<1[/itex] and [itex]0<\theta<\infty[/itex]. Let [itex]Y = -2\theta \ln X[/itex]. How is [itex]Y[/itex] distributed?

Homework Equations



Um... Fundamental Theorem of Calculus?

The Attempt at a Solution



So I've actually solved this, it's exponential with [itex]\theta = 2[/itex]. My question is about the answer given in the back of the book: It cryptically says [itex]M(t) = (1-2t)^{-1}[/itex] for [itex]t<1/2[/itex] which is, to my eye, a useless calculation of the moment-generating function. WTF? Why is this here? Ideas?

Thank you.
 
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  • #2
What's "\"?
 
  • #3
That is a slash. I'm not 100% sure why you ask, but I think maybe it's because you're confused about how to read the 1/2 (one half).
 
  • #4
transformation using the mgf approach?
 
  • #5
Addem said:

Homework Statement



(From Probability and Statistical Inference, Hogg and Tanis, Eighth Edition, 5.1-5)

The p.d.f. of X is [itex]f(x) = \theta x^{\theta - 1}[/itex] for [itex]0<x<1[/itex] and [itex]0<\theta<\infty[/itex]. Let [itex]Y = -2\theta \ln X[/itex]. How is [itex]Y[/itex] distributed?

Homework Equations



Um... Fundamental Theorem of Calculus?

The Attempt at a Solution



So I've actually solved this, it's exponential with [itex]\theta = 2[/itex]. My question is about the answer given in the back of the book: It cryptically says [itex]M(t) = (1-2t)^{-1}[/itex] for [itex]t<1/2[/itex] which is, to my eye, a useless calculation of the moment-generating function. WTF? Why is this here? Ideas?

Thank you.


One way do get a density is to get its MGF and hope you obtain a familiar, recognizable form. The form they gave you IS familiar. Of course, you are free to do the problem some other way.

RGV
 

What is a distribution function in statistics?

A distribution function, also known as a cumulative distribution function (CDF), is a mathematical function that shows the probability that a random variable takes on a certain value or falls within a certain range of values. It is used to describe the probability distribution of a random variable in a statistical model.

What is the purpose of using distribution functions in statistics?

Distribution functions are used to analyze and interpret data in order to make predictions and draw conclusions. They allow us to understand the probability of different outcomes and assess the reliability of our data.

What types of statistical distributions use distribution functions?

Many different distributions use distribution functions, including the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. These distributions are commonly used in statistical analysis for a variety of applications.

How do you calculate the probability using a distribution function?

The probability can be calculated by finding the area under the curve of the distribution function. This can be done using mathematical formulas or by using statistical software. The probability is represented by the y-axis of the graph and the values of the random variable are represented by the x-axis.

Can distribution functions be used to make predictions?

Yes, distribution functions can be used to make predictions about the likelihood of certain outcomes. By analyzing the distribution function, we can estimate the probability of a particular event occurring and use that information to make informed decisions.

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